Overview

Dataset statistics

Number of variables9
Number of observations4177
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory465.1 KiB
Average record size in memory114.0 B

Variable types

Categorical1
Numeric8

Alerts

diameter is highly overall correlated with height and 6 other fieldsHigh correlation
height is highly overall correlated with diameter and 6 other fieldsHigh correlation
length is highly overall correlated with diameter and 6 other fieldsHigh correlation
rings is highly overall correlated with diameter and 6 other fieldsHigh correlation
shell weight is highly overall correlated with diameter and 6 other fieldsHigh correlation
shucked weight is highly overall correlated with diameter and 6 other fieldsHigh correlation
viscera weight is highly overall correlated with diameter and 6 other fieldsHigh correlation
whole weight is highly overall correlated with diameter and 6 other fieldsHigh correlation

Reproduction

Analysis started2026-02-03 02:31:18.160077
Analysis finished2026-02-03 02:31:22.800226
Duration4.64 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

sex
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
M
1528 
I
1342 
F
1307 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4177
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowM
5th rowI

Common Values

ValueCountFrequency (%)
M1528
36.6%
I1342
32.1%
F1307
31.3%

Length

2026-02-03T03:31:22.852808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-03T03:31:22.908346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m1528
36.6%
i1342
32.1%
f1307
31.3%

Most occurring characters

ValueCountFrequency (%)
M1528
36.6%
I1342
32.1%
F1307
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M1528
36.6%
I1342
32.1%
F1307
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M1528
36.6%
I1342
32.1%
F1307
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M1528
36.6%
I1342
32.1%
F1307
31.3%

length
Real number (ℝ)

High correlation 

Distinct134
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5239921
Minimum0.075
Maximum0.815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:22.964162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.295
Q10.45
median0.545
Q30.615
95-th percentile0.69
Maximum0.815
Range0.74
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.12009291
Coefficient of variation (CV)0.2291884
Kurtosis0.064620974
Mean0.5239921
Median Absolute Deviation (MAD)0.08
Skewness-0.63987327
Sum2188.715
Variance0.014422308
MonotonicityNot monotonic
2026-02-03T03:31:23.034559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62594
 
2.3%
0.5594
 
2.3%
0.57593
 
2.2%
0.5892
 
2.2%
0.687
 
2.1%
0.6287
 
2.1%
0.581
 
1.9%
0.5779
 
1.9%
0.6378
 
1.9%
0.6175
 
1.8%
Other values (124)3317
79.4%
ValueCountFrequency (%)
0.0751
 
< 0.1%
0.111
 
< 0.1%
0.132
 
< 0.1%
0.1351
 
< 0.1%
0.142
 
< 0.1%
0.151
 
< 0.1%
0.1553
0.1%
0.164
0.1%
0.1655
0.1%
0.173
0.1%
ValueCountFrequency (%)
0.8151
 
< 0.1%
0.81
 
< 0.1%
0.782
 
< 0.1%
0.7752
 
< 0.1%
0.773
 
0.1%
0.7652
 
< 0.1%
0.762
 
< 0.1%
0.7553
 
0.1%
0.758
0.2%
0.7455
0.1%

diameter
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40788125
Minimum0.055
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.102563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.22
Q10.35
median0.425
Q30.48
95-th percentile0.545
Maximum0.65
Range0.595
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.099239866
Coefficient of variation (CV)0.24330578
Kurtosis-0.045475581
Mean0.40788125
Median Absolute Deviation (MAD)0.065
Skewness-0.60919814
Sum1703.72
Variance0.009848551
MonotonicityNot monotonic
2026-02-03T03:31:23.172364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45139
 
3.3%
0.475120
 
2.9%
0.4111
 
2.7%
0.5110
 
2.6%
0.47100
 
2.4%
0.4891
 
2.2%
0.45590
 
2.2%
0.4689
 
2.1%
0.4487
 
2.1%
0.48583
 
2.0%
Other values (101)3157
75.6%
ValueCountFrequency (%)
0.0551
 
< 0.1%
0.091
 
< 0.1%
0.0951
 
< 0.1%
0.12
 
< 0.1%
0.1054
0.1%
0.114
0.1%
0.1152
 
< 0.1%
0.125
0.1%
0.1257
0.2%
0.138
0.2%
ValueCountFrequency (%)
0.651
 
< 0.1%
0.633
 
0.1%
0.6251
 
< 0.1%
0.621
 
< 0.1%
0.6151
 
< 0.1%
0.611
 
< 0.1%
0.6053
 
0.1%
0.68
0.2%
0.5954
0.1%
0.596
0.1%

height
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395164
Minimum0
Maximum1.13
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.241130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.115
median0.14
Q30.165
95-th percentile0.2
Maximum1.13
Range1.13
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.041827057
Coefficient of variation (CV)0.29980029
Kurtosis76.025509
Mean0.1395164
Median Absolute Deviation (MAD)0.025
Skewness3.1288174
Sum582.76
Variance0.0017495027
MonotonicityNot monotonic
2026-02-03T03:31:23.306569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15267
 
6.4%
0.14220
 
5.3%
0.155217
 
5.2%
0.175211
 
5.1%
0.16205
 
4.9%
0.125202
 
4.8%
0.165193
 
4.6%
0.135189
 
4.5%
0.145182
 
4.4%
0.12169
 
4.0%
Other values (41)2122
50.8%
ValueCountFrequency (%)
02
 
< 0.1%
0.011
 
< 0.1%
0.0152
 
< 0.1%
0.022
 
< 0.1%
0.0255
 
0.1%
0.036
 
0.1%
0.0356
 
0.1%
0.0413
0.3%
0.04511
0.3%
0.0518
0.4%
ValueCountFrequency (%)
1.131
 
< 0.1%
0.5151
 
< 0.1%
0.253
 
0.1%
0.244
 
0.1%
0.2356
 
0.1%
0.2310
 
0.2%
0.22513
0.3%
0.2217
0.4%
0.21531
0.7%
0.2123
0.6%

whole weight
Real number (ℝ)

High correlation 

Distinct2429
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82874216
Minimum0.002
Maximum2.8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.369796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.1259
Q10.4415
median0.7995
Q31.153
95-th percentile1.6949
Maximum2.8255
Range2.8235
Interquartile range (IQR)0.7115

Descriptive statistics

Standard deviation0.49038902
Coefficient of variation (CV)0.59172689
Kurtosis-0.023643504
Mean0.82874216
Median Absolute Deviation (MAD)0.3565
Skewness0.53095856
Sum3461.656
Variance0.24048139
MonotonicityNot monotonic
2026-02-03T03:31:23.434414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22258
 
0.2%
0.47757
 
0.2%
0.1967
 
0.2%
0.977
 
0.2%
1.13457
 
0.2%
0.186
 
0.1%
0.67656
 
0.1%
0.58056
 
0.1%
0.4946
 
0.1%
0.32456
 
0.1%
Other values (2419)4111
98.4%
ValueCountFrequency (%)
0.0021
< 0.1%
0.0081
< 0.1%
0.01051
< 0.1%
0.0131
< 0.1%
0.0141
< 0.1%
0.01452
< 0.1%
0.0151
< 0.1%
0.01551
< 0.1%
0.01751
< 0.1%
0.0182
< 0.1%
ValueCountFrequency (%)
2.82551
< 0.1%
2.77951
< 0.1%
2.6571
< 0.1%
2.5551
< 0.1%
2.551
< 0.1%
2.5481
< 0.1%
2.5261
< 0.1%
2.51551
< 0.1%
2.50851
< 0.1%
2.5051
< 0.1%

shucked weight
Real number (ℝ)

High correlation 

Distinct1515
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35936749
Minimum0.001
Maximum1.488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.512709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0524
Q10.186
median0.336
Q30.502
95-th percentile0.7402
Maximum1.488
Range1.487
Interquartile range (IQR)0.316

Descriptive statistics

Standard deviation0.22196295
Coefficient of variation (CV)0.61764894
Kurtosis0.59512368
Mean0.35936749
Median Absolute Deviation (MAD)0.1585
Skewness0.71909792
Sum1501.078
Variance0.049267551
MonotonicityNot monotonic
2026-02-03T03:31:23.585719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17511
 
0.3%
0.250510
 
0.2%
0.3029
 
0.2%
0.29459
 
0.2%
0.0969
 
0.2%
0.219
 
0.2%
0.1659
 
0.2%
0.4199
 
0.2%
0.29
 
0.2%
0.20259
 
0.2%
Other values (1505)4084
97.8%
ValueCountFrequency (%)
0.0011
 
< 0.1%
0.00251
 
< 0.1%
0.00452
< 0.1%
0.0053
0.1%
0.00552
< 0.1%
0.00653
0.1%
0.0071
 
< 0.1%
0.00754
0.1%
0.0081
 
< 0.1%
0.00851
 
< 0.1%
ValueCountFrequency (%)
1.4881
< 0.1%
1.3511
< 0.1%
1.34851
< 0.1%
1.2531
< 0.1%
1.24551
< 0.1%
1.23952
< 0.1%
1.2321
< 0.1%
1.19651
< 0.1%
1.19451
< 0.1%
1.17051
< 0.1%

viscera weight
Real number (ℝ)

High correlation 

Distinct880
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18059361
Minimum0.0005
Maximum0.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.655711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.027
Q10.0935
median0.171
Q30.253
95-th percentile0.3796
Maximum0.76
Range0.7595
Interquartile range (IQR)0.1595

Descriptive statistics

Standard deviation0.10961425
Coefficient of variation (CV)0.60696639
Kurtosis0.084011749
Mean0.18059361
Median Absolute Deviation (MAD)0.0795
Skewness0.59185215
Sum754.3395
Variance0.012015284
MonotonicityNot monotonic
2026-02-03T03:31:23.723842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.171515
 
0.4%
0.19614
 
0.3%
0.057513
 
0.3%
0.03713
 
0.3%
0.06113
 
0.3%
0.219513
 
0.3%
0.09912
 
0.3%
0.09612
 
0.3%
0.1512
 
0.3%
0.026512
 
0.3%
Other values (870)4048
96.9%
ValueCountFrequency (%)
0.00052
 
< 0.1%
0.0021
 
< 0.1%
0.00252
 
< 0.1%
0.0033
0.1%
0.00353
0.1%
0.0041
 
< 0.1%
0.00454
0.1%
0.0057
0.2%
0.00556
0.1%
0.0062
 
< 0.1%
ValueCountFrequency (%)
0.761
< 0.1%
0.64151
< 0.1%
0.591
< 0.1%
0.5751
< 0.1%
0.57451
< 0.1%
0.5641
< 0.1%
0.551
< 0.1%
0.5412
< 0.1%
0.52651
< 0.1%
0.5261
< 0.1%

shell weight
Real number (ℝ)

High correlation 

Distinct926
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23883086
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.801068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0384
Q10.13
median0.234
Q30.329
95-th percentile0.48
Maximum1.005
Range1.0035
Interquartile range (IQR)0.199

Descriptive statistics

Standard deviation0.13920267
Coefficient of variation (CV)0.58285043
Kurtosis0.53192613
Mean0.23883086
Median Absolute Deviation (MAD)0.0995
Skewness0.62092683
Sum997.5965
Variance0.019377383
MonotonicityNot monotonic
2026-02-03T03:31:23.877910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27543
 
1.0%
0.2542
 
1.0%
0.31540
 
1.0%
0.18540
 
1.0%
0.26540
 
1.0%
0.1737
 
0.9%
0.28537
 
0.9%
0.17536
 
0.9%
0.2236
 
0.9%
0.336
 
0.9%
Other values (916)3790
90.7%
ValueCountFrequency (%)
0.00151
 
< 0.1%
0.0031
 
< 0.1%
0.00351
 
< 0.1%
0.0042
 
< 0.1%
0.00512
0.3%
0.0061
 
< 0.1%
0.00651
 
< 0.1%
0.0071
 
< 0.1%
0.00751
 
< 0.1%
0.0084
 
0.1%
ValueCountFrequency (%)
1.0051
 
< 0.1%
0.8971
 
< 0.1%
0.8852
< 0.1%
0.851
 
< 0.1%
0.8151
 
< 0.1%
0.79751
 
< 0.1%
0.781
 
< 0.1%
0.761
 
< 0.1%
0.7261
 
< 0.1%
0.7253
0.1%

rings
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9336845
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2026-02-03T03:31:23.942616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median9
Q311
95-th percentile16
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.224169
Coefficient of variation (CV)0.3245693
Kurtosis2.3306874
Mean9.9336845
Median Absolute Deviation (MAD)2
Skewness1.1141019
Sum41493
Variance10.395266
MonotonicityNot monotonic
2026-02-03T03:31:24.001880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9689
16.5%
10634
15.2%
8568
13.6%
11487
11.7%
7391
9.4%
12267
 
6.4%
6259
 
6.2%
13203
 
4.9%
14126
 
3.0%
5115
 
2.8%
Other values (18)438
10.5%
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
315
 
0.4%
457
 
1.4%
5115
 
2.8%
6259
 
6.2%
7391
9.4%
8568
13.6%
9689
16.5%
10634
15.2%
ValueCountFrequency (%)
291
 
< 0.1%
272
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
242
 
< 0.1%
239
 
0.2%
226
 
0.1%
2114
0.3%
2026
0.6%
1932
0.8%

Interactions

2026-02-03T03:31:22.158200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.516801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.082429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.597479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.111100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.590652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.122212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.714219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.235089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.585683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.140611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.685337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.166389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.668461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.185969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.768403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.315240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.646053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.196696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.748653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.222447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.736892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.245425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.829861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.373556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.764943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.254504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.811147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.296931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.800628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.302881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.883216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.430991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.826209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.338294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.872472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.358485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.861490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.361513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.935362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.490936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.889606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.404852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.937350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.418939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.927096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.530534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.992877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.553974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:18.952247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.472217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.001745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.476784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.994344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.595052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.049035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.610397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.013003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:19.531765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.056990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:20.531756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.060136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:21.653659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-03T03:31:22.100905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-03T03:31:24.059013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
diameterheightlengthringssexshell weightshucked weightviscera weightwhole weight
diameter1.0000.8960.9830.6230.4020.9540.9500.9480.971
height0.8961.0000.8880.6580.3600.9210.8740.9010.916
length0.9830.8881.0000.6040.3940.9480.9570.9530.973
rings0.6230.6580.6041.0000.3560.6920.5390.6140.631
sex0.4020.3600.3940.3561.0000.4130.3930.4240.424
shell weight0.9540.9210.9480.6920.4131.0000.9180.9380.969
shucked weight0.9500.8740.9570.5390.3930.9181.0000.9480.977
viscera weight0.9480.9010.9530.6140.4240.9380.9481.0000.975
whole weight0.9710.9160.9730.6310.4240.9690.9770.9751.000

Missing values

2026-02-03T03:31:22.689725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-03T03:31:22.748739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightrings
0M0.4550.3650.0950.51400.22450.10100.15015
1M0.3500.2650.0900.22550.09950.04850.0707
2F0.5300.4200.1350.67700.25650.14150.2109
3M0.4400.3650.1250.51600.21550.11400.15510
4I0.3300.2550.0800.20500.08950.03950.0557
5I0.4250.3000.0950.35150.14100.07750.1208
6F0.5300.4150.1500.77750.23700.14150.33020
7F0.5450.4250.1250.76800.29400.14950.26016
8M0.4750.3700.1250.50950.21650.11250.1659
9F0.5500.4400.1500.89450.31450.15100.32019
sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightrings
4167M0.5000.3800.1250.57700.26900.12650.15359
4168F0.5150.4000.1250.61500.28650.12300.17658
4169M0.5200.3850.1650.79100.37500.18000.181510
4170M0.5500.4300.1300.83950.31550.19550.240510
4171M0.5600.4300.1550.86750.40000.17200.22908
4172F0.5650.4500.1650.88700.37000.23900.249011
4173M0.5900.4400.1350.96600.43900.21450.260510
4174M0.6000.4750.2051.17600.52550.28750.30809
4175F0.6250.4850.1501.09450.53100.26100.296010
4176M0.7100.5550.1951.94850.94550.37650.495012